class_docstr: A deterministic model defined by a single sample `w`.
  
  Given a base model `f` and a fixed sample `w`, the model always outputs
  
      y = f_mean(x) + f_stddev(x) * w
  
  We assume the outcomes are uncorrelated here.
code: def forward(self, X: Tensor) -> Tensor:
      post = self.model.posterior(X)
      return post.mean + torch.sqrt(
          post.variance + self.jitter
      ) * self.w.to(X)
  
imports: from __future__ import annotations
  from abc import abstractmethod
  from typing import Callable, List, Optional, Union
  import torch
  from botorch.models.ensemble import EnsembleModel
  from botorch.models.model import Model
  from torch import Tensor
label_desc: Use image node_id 2 for calling the FixedSingleSampleModel obj's underlying member method code with example usage: obj.forward(X) and returns: unknown